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CVE-2022-21732

Memory exhaustion in Tensorflow

Summary

Memory exhaustion in Tensorflow

Original description

The description as published by the source.

Tensorflow is an Open Source Machine Learning Framework. The implementation of ThreadPoolHandle can be used to trigger a denial of service attack by allocating too much memory. This is because the num_threads argument is only checked to not be negative, but there is no upper bound on its value. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Source: OSV.dev (Open Source Vulnerabilities) NVD GitHub Advisory OSV

Affected packages

PyPI tensorflow
[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"fixed":"2.7.1"}]}]
PyPI tensorflow-cpu
[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"fixed":"2.7.1"}]}]
PyPI tensorflow-gpu
[{"type":"ECOSYSTEM","events":[{"introduced":"2.7.0"},{"fixed":"2.7.1"}]}]
Bitnami tensorflow
[{"type":"SEMVER","events":[{"introduced":"0"},{"fixed":"2.5.3"},{"introduced":"2.6.0"},{"fixed":"2.6.3"},{"introduced":"2.7.0"},{"fixed":"2.7.1"}]}]

References

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